Authors in [1]} presented a high level overview on the optimization techniques of deep neural networks (DNNs) for tinyML on device inferences. TinyML model optimization includes different algorithms of Parameter Search, Sparsification and Quantization techniques. Element-wise pruning[2]}, Structured Pruning [3]}, [4]} these techniques reduces the unimportant weights and compress the models to be implemented on tinyML devices. Extreme low precision quantization [5]}, [6]} and mixed precision quantization [7]}, [8]}, [9]}, [10]}, [11]} is adopted by the researchers to decrease the memory requirements of the DNN models. MCUNet [12]}, MicroNets [13]} are proposed to deploy DNN models on micro-controller units (MCU).
Recently, multimodal learning attracts researchers to improve the classification accuracy of the models integrating different modalities of data fusion [14]}, [15]}, [16]}. However, implementation of multimodal learning into resource constrained tiny hardware is very limited due to its large model sizes. We present a novel multimodal learning framework TinyM\(^2\) Net which is system algorithm co-designed and different model compression techniques were implemented to compress the large multimodal models to be implemented on tiny devices.
